Longitudinal studies in medicine are faced with new analysis challenges due to continually advancing measurement and database technologies. Specifically, innovations in molecular assays, medical imaging, and psychological assessment have generated numerous new putative markers of disease progression. Also, advances in electronic data recording now allow longitudinal investigations to collect observational data measuring changes in outcomes and changes in treatments where dynamic treatment covariates are driven by current clinical guidelines, by unfolding patient health characteristics, or other factors. The overall goals of this proposal are to develop statistical methodology and software tools for analyzing modern longitudinal biomedical data. The specific areas of emphasis are: 1. Repeated measures and time-dependent accuracy. Biomarkers are measurements that characterize specific aspects of patient health status. This aim will develop semi-parametric and non-parametric statistical methods to estimate the ability of prognostic scores or markers to accurately predict event times as characterized by time-dependent measures of sensitivity and specificity. 2. Observational longitudinal data and time-dependent exposure. Longitudinal studies now routinely collect both patient health information and changing covariate (treatment, exposure) data. This aim will develop statistical methods that can be used to estimate causal effects of exposure, and to evaluate the ability of marker values to guide the choice or timing of treatment.